Shipra Agrawal

Artificial Intelligence · 2004–2022

A researcher in the Science corpus: 6 papers; trusts 52 researchers and is trusted by 95 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #2,258 of 35,345 by trust here, #7,010 by citations
• Reinforcement Learning — 45 citations into it; #435 of 2,242 by trust here, #1,204 by citations
• Management Science and Operations Research — 44 citations into it; #275 of 11,946 by trust here, #748 by citations
• Probabilistic & Bayesian Methods — 15 citations into it
• Computer Networks and Communications — 14 citations into it; #5,198 of 9,886 by trust here, #8,191 by citations
• Statistics and Probability — 13 citations into it
• Statistical Learning Theory — 8 citations into it
• Algorithms & Complexity — 7 citations into it

Whom Shipra Agrawal trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. T. L. Lai Andherbertrobbins
2. Malcolm J. A. Strens
3. Emilie Kaufmann
4. Peter Auer
5. Aurélien Garivier
6. Sébastien Bubeck
7. Steven L. Scott
8. Nicolò Cesa-Bianchi
9. Jean-Yves Audibert
10. Daniel Russo

Who trusts Shipra Agrawal overall

1. Daniel Russo
2. Ian Osband
3. Benjamin Van Roy
4. Aleksandrs Slivkins
5. Zihan Zhang
6. Alberto Bietti
7. Navin Goyal
8. Maria Dimakopoulou
9. Tor Lattimore
10. Emilie Kaufmann

Papers from Shipra Agrawal's perspective

Ordered by how strongly the researchers Shipra Agrawal trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Reinforcement Learning: An Introduction
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Thompson Sampling for Contextual Bandits with Linear Payoffs (2012) — 1,155 citations
• Optimistic posterior sampling for reinforcement learning: worst-case regret bounds (2022) — 240 citations
• Further Optimal Regret Bounds for Thompson Sampling (2012) — 469 citations
• A Dynamic Near-Optimal Algorithm for Online Linear Programming (2009) — 341 citations
• A framework for high-accuracy privacy-preserving mining (2004) — 234 citations
• Near-Optimal Regret Bounds for Thompson Sampling (2017) — 200 citations